tag:joss.theoj.org,2005:/papers/tagged/ensemblingJournal of Open Source Software2023-12-18T16:42:37ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/49122023-12-18T16:42:37Z2023-12-19T00:00:28ZlibEnsemble: A complete Python toolkit for dynamic ensembles of calculationsacceptedv1.0.02023-11-01 13:13:50 UTC922023-12-18 16:42:37 UTC820236031StephenHudsonMathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA0000-0002-7500-6138JeffreyLarsonMathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA0000-0001-9924-2082John-LukeNavarroMathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA0000-0002-9916-9038StefanM.WildAMCR Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA, Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA0000-0002-6099-277210.21105/joss.06031https://doi.org/10.5281/zenodo.10391387Python, MATLAB, Perl, Chttps://joss.theoj.org/papers/10.21105/joss.06031.pdfensemble workflows, optimization and learningtag:joss.theoj.org,2005:Paper/42182023-06-16T16:37:49Z2023-06-17T09:15:49ZMelissa: coordinating large-scale ensemble runs for deep learning and sensitivity analysesacceptedV1.0.02023-02-17 13:28:46 UTC862023-06-16 16:37:49 UTC820235291MarcSchoulerUniv. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France0000-0002-3708-4135RobertAlexanderCaulkUniv. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France0000-0001-5618-8629LucasMeyerUniv. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France, Industrial AI Laboratory SINCLAIR, EDF Lab Paris-Saclay, France0000-0001-5386-5997ThéophileTerrazUniv. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, FranceChristophConradsUniv. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, FranceSebastianFriedemannUniv. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, FranceAchalAgarwalUniv. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France0000-0002-3216-4769JuanManuelBaldonadoUniv. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, FranceBartłomiejPogodzińskiInstitute of Bioorganic Chemistry Polish Academy of Sciences, Poznań Supercomputing and Networking CenterAnnaSekułaInstitute of Bioorganic Chemistry Polish Academy of Sciences, Poznań Supercomputing and Networking Center0000-0003-3524-3160AlejandroRibesIndustrial AI Laboratory SINCLAIR, EDF Lab Paris-Saclay, FranceBrunoRaffinUniv. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France10.21105/joss.05291https://doi.org/10.5281/zenodo.8046630C, Fortran, Pythonhttps://joss.theoj.org/papers/10.21105/joss.05291.pdfsupercomputing, sensitivity analysis, deep learning, distributed systems, orchestrationtag:joss.theoj.org,2005:Paper/39312022-12-15T20:20:06Z2022-12-16T00:00:57ZEnsembleKalmanProcesses.jl: Derivative-free ensemble-based model calibrationacceptedv0.13.02022-09-30 23:33:10 UTC802022-12-15 20:20:06 UTC720224869OliverR. a.DunbarDivision of Geological and Planetary Sciences, California Institute of Technology0000-0001-7374-0382IgnacioLopez-GomezDivision of Geological and Planetary Sciences, California Institute of Technology0000-0002-7255-5895AlfredoGarbuno-IñigoDepartment of Statistics, Mexico Autonomous Institute of Technology0000-0003-3279-619XDanielZhengyuHuangDivision of Geological and Planetary Sciences, California Institute of Technology0000-0001-6072-9352EviatarBachDivision of Geological and Planetary Sciences, California Institute of Technology0000-0002-9725-0203Jin-longWuDepartment of Mechanical Engineering, University of Wisconsin-Madison0000-0001-7438-422810.21105/joss.04869https://doi.org/10.5281/zenodo.7407193Juliahttps://joss.theoj.org/papers/10.21105/joss.04869.pdfjulia, optimization, bayesian, data assimilationtag:joss.theoj.org,2005:Paper/36472022-07-06T15:37:27Z2022-07-07T00:01:02Zstacks: Stacked Ensemble Modeling with Tidy Data Principlesacceptedv0.2.32022-06-08 16:51:58 UTC752022-07-06 15:37:27 UTC720224471SimonP.CouchRStudio PBC0000-0001-5676-5107MaxKuhnRStudio PBC10.21105/joss.04471https://doi.org/10.5281/zenodo.6800026Rhttps://joss.theoj.org/papers/10.21105/joss.04471.pdfdata science, tidyverse, model stacking, ensemblingtag:joss.theoj.org,2005:Paper/18712020-11-07T08:20:12Z2022-01-18T11:52:28ZMLJ: A Julia package for composable machine learningacceptedv0.11.62020-07-21 21:29:04 UTC552020-11-07 08:20:12 UTC520202704AnthonyD.BlaomUniversity of Auckland, New Zealand, New Zealand eScience Infrastructure, New Zealand, Alan Turing Institute, London, United Kingdom0000-0001-6689-886XFranzKiralyAlan Turing Institute, London, United Kingdom, University College London, United Kingdom0000-0002-9254-793XThibautLienartAlan Turing Institute, London, United KingdomYiannisSimillidesImperial College London, United Kingdom0000-0002-0287-8699DiegoArenasUniversity of St Andrews, St Andrews, United Kingdom0000-0001-7829-6102SebastianJ.VollmerAlan Turing Institute, London, United Kingdom, University of Warwick, United Kingdom0000-0002-9025-075310.21105/joss.02704https://doi.org/10.5281/zenodo.4178917Jupyter Notebook, Juliahttps://joss.theoj.org/papers/10.21105/joss.02704.pdfMachine Learning, model composition, stacking, ensembling, hyper-parameter tuningtag:joss.theoj.org,2005:Paper/19182020-09-26T12:21:02Z2021-02-15T11:30:12Zemba: R package for analysis and visualization of biomarkers in boolean model ensemblesacceptedv0.1.62020-07-28 19:45:33 UTC532020-09-26 12:21:02 UTC520202583JohnZobolasDepartment of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway, Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway0000-0002-3609-8674MartinKuiperDepartment of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway0000-0002-1171-9876ÅsmundFlobakDepartment of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway, The Cancer Clinic, St. Olav’s Hospital, Trondheim, Norway0000-0002-3357-425X10.21105/joss.02583https://doi.org/10.5281/zenodo.4043085Rhttps://joss.theoj.org/papers/10.21105/joss.02583.pdfboolean networks, logical modeling, biomarkers, mechanistic models, drug synergies, anti-cancer drug combinations, druglogicstag:joss.theoj.org,2005:Paper/13022019-11-17T18:29:53Z2022-01-18T11:37:45Zemcee v3: A Python ensemble sampling toolkit for affine-invariant MCMCacceptedv3.0.12019-10-28 18:00:31 UTC432019-11-17 18:29:53 UTC420191864DanielForeman-MackeyCenter for Computational Astrophysics, Flatiron Institute0000-0002-9328-5652WillM.FarrCenter for Computational Astrophysics, Flatiron Institute, Department of Physics and Astronomy, Stony Brook University, United States0000-0003-1540-8562ManodeepSinhaCentre for Astrophysics & Supercomputing, Swinburne University of Technology, Australia, ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D)0000-0002-4845-1228AnneM.ArchibaldUniversity of Newcastle0000-0003-0638-3340DavidW.HoggCenter for Computational Astrophysics, Flatiron Institute, Center for Cosmology and Particle Physics, Department of Physics, New York University0000-0003-2866-9403JeremyS.SandersMax Planck Institute for Extraterrestrial Physics0000-0003-2189-4501JoeZuntzInstitute for Astronomy, University of Edinburgh, Edinburgh, EH9 3HJ, UK0000-0001-9789-9646PeterK. g.WilliamsCenter for Astrophysics | Harvard & Smithsonian, American Astronomical Society0000-0003-3734-3587AndrewR. j.NelsonAustralian Nuclear Science and Technology Organisation, NSW, Australia0000-0002-4548-3558Miguelde Val-BorroPlanetary Science Institute, 1700 East Fort Lowell Rd., Suite 106, Tucson, AZ 85719, USA0000-0002-0455-9384TobiasErhardtClimate and Environmental Physics and Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland0000-0002-6683-6746IlyaPashchenkoP.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, Russia0000-0002-9404-7023OriolAbrilPlaUniversitat Pompeu Fabra, Barcelona0000-0002-1847-948110.21105/joss.01864https://doi.org/10.5281/zenodo.3543502Pythonhttps://joss.theoj.org/papers/10.21105/joss.01864.pdfastronomytag:joss.theoj.org,2005:Paper/9122019-03-29T11:09:29Z2021-02-15T11:32:27Zrrcf: Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streamsacceptedv0.22019-03-04 21:56:28 UTC352019-03-29 11:09:29 UTC420191336MatthewD.BartosDepartment of Civil and Environmental Engineering, University of Michigan0000-0001-6421-222XAbhiramMullapudiDepartment of Civil and Environmental Engineering, University of Michigan0000-0001-8141-3621SaraC.TroutmanDepartment of Civil and Environmental Engineering, University of Michigan0000-0002-6809-795910.21105/joss.01336https://doi.org/10.5281/zenodo.2613881Pythonhttps://joss.theoj.org/papers/10.21105/joss.01336.pdfoutlier detection, machine learning, ensemble methods, random foreststag:joss.theoj.org,2005:Paper/4392018-04-22T13:05:17Z2021-02-15T11:33:32ZMLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stackacceptedv0.11.02018-03-15 16:08:53 UTC242018-04-22 13:05:17 UTC32018638SebastianRaschkaMichigan State University0000-0001-6989-449310.21105/joss.00638https://doi.org/10.5281/zenodo.1226560Pythonhttps://joss.theoj.org/papers/10.21105/joss.00638.pdfmachine learning, data science, association rule mining, ensemble learning, feature selectiontag:joss.theoj.org,2005:Paper/412016-12-20T00:00:00Z2021-02-15T11:34:28Zfuse: An R package for ensemble Hydrological Modellingacceptedv3.0.02016-08-16 15:38:14 UTC82016-12-20 00:00:00 UTC1201652ClaudiaVitoloImperial College London0000-0002-4252-1176PeterWellsLutra ConsultingMartinDobiasLutra ConsultingWouterBuytaertImperial College London0000-0001-6994-445410.21105/joss.00052https://doi.org/10.5281/zenodo.212822R, C, C++https://joss.theoj.org/papers/10.21105/joss.00052.pdfhydrological modelling